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Some essential issues about modeling of reverse logistics (RL) systems and product recovery networks include consideration of the qualities of the returned products, taking into account uncertainty and integrating the forward and reverse flows. The purpose of this paper is to develop the integrated RL model, which focuses on the control of inventory and production planning problems in a case of uncertainty in demand, quantities and qualities of returns.

The model involves a forward production route, three alternative recovery routes and a disposal route. Various levels of qualities are considered for returned products. A fuzzy mixed integer programming model (FMIP) is developed to provide a solution for the problems of production planning and inventory control. After maximizing the satisfaction degree, different solutions can have the same maximum. Moreover, policies that use all recovery routes and reduce the overall uncertainty have no chance to be chosen. To tackle these problems, a two-phase approach method is applied.

According to the results of the numerical example, using different and appropriate recovery options based on the quality of returns can significantly decrease the recovery costs. Similarly, it is shown that the two-phase approach can be an effective and efficient method to reach a satisfactory solution for such problems.

In this study, after maximizing the FMIP model, a two-phase approach ‒ as a novel optimization technique in this research ‒ is employed to achieve a desirable solution.

The complexity of manufacturing systems, on-going production and existing constraints on the shop floor remain among the main challenges for the analysis, design and development of the models in product, process and factory domains. The potential of different virtual factory (VF) tools and approaches to support simultaneous engineering for the design, and development of these domains has been addressed in the literature. To fulfil this potential, there is a need for an approach which integrates the product, process and production systems for designing and developing VF and its validation in real-life cases. This paper aims to present an integrated design approach for VF design and development, as well as a demonstration implemented in a wind turbine manufacturing plant.

As the research calls for instrumental knowledge to discover the effects of intervention on the operations of an enterprise, design science research methodology is considered to be a well-suited methodology for exploring practical usefulness of a generic design to close the theory–practice gap. The study was planned as an exploratory research activity which encompassed the simultaneous design and development of artefacts and retrospective analysis of the design and implementation processes. The extended VF concept, architecture, a demonstration and procedures followed during the research work are presented and evaluated.

The artefacts (models and methods) and the VF demonstrator, which was evaluated by industry experts and scholars based on the role of the VF in improving the performance in the evaluation and reconfiguration of new or existing factories, reduce the ramp-up and design times, supporting management decisions. Preliminary results are presented and discussed.

The concept VF model, its architecture and general methodology as an integrated design and development approach, can be adopted and used for VF design and development both for discrete and continuous manufacturing plants. The development and demonstration were limited, however, because real-time synchronisation, 3D laser scanning data and a commonly shared data model, to enable the integration of different VF tools, were not achievable.

The paper presents a novel VF concept and architecture, which integrates product, process and production systems. Moreover, design and development methods of the concept and its demonstration for a wind turbine manufacturing plant are presented. The paper, therefore, contributes to the information systems and manufacturing engineering field by identifying a novel concept and approach to the effective design and development of a VF and its function in the analysis, design and development of manufacturing systems.

The set of compositional approaches to product space development is expanded to include confirmatory methods. Specifically, describes and compares product space development (perceptual mapping) via confirmatory factor analysis and partial least squares with the aid of an empirical example. Both of these procedures are widely used in causal or structural equation modelling. Since they tend to be confirmatory extensions to factor analysis and principal components analysis, the approaches are also well suited to the development of product spaces. Confirmatory approaches have several advantages over exploratory approaches including the incorporation of prior knowledge, the elimination of rotational indeterminacy, and the use of a wide variety of measurement tools to assess the reliability and validity of model results.

Within the Mondragón Valley – an industrial heartland in the Basque Country, in the North of Spain – there has been a growing need for a practical model of the new product development (NPD) process which fulfils a variety of needs, notably the integration of strategy and technology considerations.

The paper is based on a review of the state of the art in the NPD field, focusing on the use and limitations of both established and newer models for guiding product development. This is combined with the experiences of managing real NPD projects for companies in the Mondragón Corporación Cooperativa over the past several years.

The paper presents the Ikerlan new product creation (NPC) model, designed primarily in response to area needs, which attempts to combine the most useful aspects of existing NPD models.

In the first instance, the model is created for use by companies in the Mondragón Valley – therefore it may not be possible to generalise findings, yet the paper may be instructive in showcasing the real industrial innovation needs of a European region. The model evolved from several notable works in the NPD community which may address these limitations in part.

The model may be used as a basic guide for aspects including NPD outsourcing, idea generation and, ultimately, reducing time to market. This is specifically tailored to companies in the Mondragón Valley. However, depending on the operating context certain aspects may be transferable to other European areas.

The paper details the industrial context, introducing the Mondragón cooperative – the largest industrial cooperative in the world with over 100 companies in seven industrial sectors – the academic context, discussing state of the art in NPD processes and models, and concludes with short industrial cases which show the potential use of the developed model. As such, insight is presented into a European area with a tradition of innovation, together with some of the challenges it faces in the years ahead. A new NPD model, combining the most useful aspects of existing models may also be of use to certain members of the academic and industrial communities.

New products are seldom really new. Yet modellers of adoption processes have largely ignored the effects of existing buying habits on first trial of a product. A standard renewal process model is shown to fit data on cumulative shoppers at a new store, in conjunction with lognormal buying frequency assumptions. The penetration curves so derived lie close to the widely used modified exponential, which therefore obtains a rationale. Such curves represent the normal rise in penetration, as a proportion of shoppers enter the market in accordance with their regular cycle of repurchase. Most products are bought in this way. Few are really new, in a sense defined and discussed. Yet model builders have persistently used the framework of adoption processes, ignoring the influence of a repurchase cycle. They have instead assumed that new product adoption is “driven” by forces such as weight of advertising. From the position of a devil's advocate, it is arguable that these assumptions are misguided. Marketing mix variables are not sufficient or adequate to explain the growth rate of products which are not genuine innovations.

Despite the large number of new product models and methods available to improve the success rate of new products, empirical evidence proves that only a few companies actually use them. This seems rather strange because today the pressure on companies to improve their new product development is high. A bottle‐neck may be the extent to which companies are aware of these models and methods. Companies may also have abandoned their use already. To investigate this, gathers empirical data from 75 industrial companies in The Netherlands. Results show that many companies are actually aware of these models and methods. However, they often do not know them by name. The average penetration level is about 30 per cent and the models and methods are used in an unfocused and less formal manner. The level of satisfaction with the performance of most models and methods is high. Furthermore, a positive relationship between the degree of use of models/methods and company′s gross profits exists.

Uncertainty in the early development of digital business startups can benefit from data-driven testing of hypotheses. Startups face uncertainty not only in product development, but also over the structure of the business model and the nature of the customer or market to address. The authors conceptualize a new model, the Lean Discovery Process (LDP), which focuses on market-based testing from the early business idea through to fully realized product stages of an innovation. The purpose of this paper is to highlight a methodology to help digital business reduce uncertainty and apply lean principles as early as possible in the development of a business concept.

Examining literature in lean startups, lean user experience and lean software development, the authors highlight underlying assumptions of existing lean models. The authors then examine the LDP using the case of raiserve, a social entrepreneurship startup that focuses on the management of cause-based voluntary service.

Existing literature focuses on product development against an assumed customer base. Early hypothesis testing can be applied to business concept development to substantially reduce cost and time to market.

Further investigation of different forms of uncertainty in digital startups can open up opportunities to further apply lean methodologies. A more extensive empirical study to measure the potential impact is warranted.

The authors conceptualize the minimum viable customer and support early testing with concepts from market research and collective intelligence. The authors demonstrate early opportunities to apply lean principles and rigorous hypothesis testing in an LDP that results in significant reductions in time and expense of product development.

The purpose of this paper is to provide a customer lifetime value (CLV) model to carefully assess and classify banking customers using individual measures and covering customers’ relationships with a portfolio of products of the company.

The proposed model comprises two sub-models: (sub-model 1) modelling and prediction of CLV in a multiproduct context using Hierarchical Bayesian models as input to (sub-model 2) a value-based segmentation specially designed to manage customers and products using the latent class regression. The model is tested using real transaction data of 1,357 customers of a bank.

This research demonstrates which drivers of customer value better predict the contribution margin and product usage for each of the products considered in order to get the CLV measure. Using this measure, the model implements a value-based segmentation, which helps banks to facilitate the process of customer management.

Previous CLV models are mostly conceptual, generalisation is one of their main concerns, are usually focussed on single product categories using aggregated customer data, and they are not design with a special emphasis on their application as support for managerial decisions. In response to these drawbacks, the proposed model will enable decision makers to improve the understanding of the value of each customer and their behaviour towards different financial products.

This paper presents a framework for supply chain decision‐making. The framework is used to gain insights into applications of modelling. Current modelling practice is reviewed through a literature survey. The principal finding is a lack of published research in the area of modelling supply chain effects in the product development phase. However, it is in the product development phase where the majority of product life‐cycle costs are determined. As a guideline for further case research, we propose an approach for integration in product life cycle modelling systems. For practitioners, we point out some major requirements for implementation. Finally, we demonstrate an early application of some of the ideas.

This study aims to develop a synthetic knowledge repository consisted of interrelated Web Ontology Language.

The ontology composes the main framework to categorize data of product life cycle with eco-design mode (PLC-EDM) and automatically infer specialists’ knowledge for data confirmation, eventually assisting the utilizations and generation of strategies toward decision-making

(i) utilization of a novel model with ontology mode for information reuse cross the different eco-design applications; (ii) generation of a sound platform toward life cycle evaluation; and (iii) implementation of the PLC-EDM model along the product generation process.

It cannot substitute an evaluation tool of life cycle. Certainly, this model does not predict the “target and range” and/or the depiction of the “utility module” that are basic activities in life cycle assessments as characterized through the international organization for standardization regulations.

As portion of this framework, a prototype Web application is presented which is applied to produce, reuse and verify knowledge of product life cycle.

By counting upon the ontology, the information conducted by the utilization is certainly semantically represented to promote the data sharing among various participants and tools. Besides, the data can be verified against possible faults by inferring over the ontology. Hence, a feasible way to a popular topic in the domain of eco-design applications extension in the industry.

The goals are: to lean on rigid modeling principles; and to promote the interoperability and diffusion of the ontology toward particular utilization demands.